Can super-diversity enhance fairness in AI systems?
Open eGovernment program, DSV, Stockholm University
2025-06-04
“Counter vetting pressure isn’t going to come, you know, from eight people sipping wine in Kettering” 1
To provide a high-level overview of:
Stages of ML model development from Kheya et al. (2024)
Fair AI system and Limitations from Buyl and De Bie (2024)
“Highly Accurate, But Still Discriminatory” (Kochling et al., 2020)
“Assessing risk, automating racism” (Benjamin, 2019)
“[T]wo contrasting research paradigms: one rooted in computer science (CS), the origin discipline of fair AI, and another one that is more socially-oriented and interdisciplinary (SOI)” (Fahimi et al., 2024)
“Equipping practitioners to recognize and address algorithmic bias and fairness debt” and “Improving bias mitigation and ethical design to address fairness debt” (de Souza Santos, 2024)
‘Super-diversity’ is a term intended to underline a level and kind of complexity surpassing anything previously experienced in a particular society due to global migration patterns. This results in wholly new and complex social formations marked by a dynamic interplay of variables. These variables co-condition integration outcomes. 2
Criticism:
Is this a theory, a concept, an approach?
Only effective for urban populations with multiple waves of migrant arrivals?
Are dimensions–variables, individual traits, or groupings/categorizations?
Is integration the antithesis of social exclusion and would a fair AI system need to be measured on how it helps with mitigating social exclusion?
Approach as mitigation strategy
Categorizations for cluster/segmentation analysis
Fairness as an integration outcome
Nowadays however, an increasing number of cities and communities can be characterized as internationalized and super-diverse with no monolithic mainstream society but a multitude of diverse groups (Vertovec 2007; Crul 2016; Grzymala-Kazlowska and Phillimore 2018). This raises the question of not only who integrates but also “into what?.” 3
How can super-diversity theory be integrated into fair AI system development to better account for the heterogeneous nature of human populations?
To provide a high-level overview of:
Issues with traditional SLR:
Benefits of adopting framework:
This becomes largely a matter subject to interpretation
Hermeneutic framework for the literature review process from Boell and Cecez-Kecmanovic (2014)
Mapping aspects from Fair AI and Super-diversity literature
Thematic analysis process sample
Thematic analysis summary where both the attempt to tag requirements, and find a need for evidence or support from another domain, is seen as the overall conceptual gap
To provide a high-level overview of:
This is derived from a comparison of fair AI limitations, their applicable stages, concepts and examples from the literature.
The main contribution of this thesis is that it shed light on a topical, compelling problem space.